Results 11 to 20 of about 110,828 (307)
Heteroscedastic Gaussian process regression [PDF]
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori
Quoc V. Le +2 more
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Hierarchical Gaussian process mixtures for regression [PDF]
Statistics and Computing, 2005 As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements.
Jian Qing Shi +2 more
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Projection pursuit Gaussian process regression
IISE Transactions, 2022 A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer from the curse of dimensionality, when the input dimension is relatively high given limited data points. Gaussian process models with additive correlation functions are scalable
Gecheng Chen, Rui Tuo
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Complex Gaussian Processes for Regression [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued one. This approach is limited. Based on the results in complex-valued linear theory and Gaussian random processes,
Rafael Boloix-Tortosa +3 more
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Gaussian process regression in the flat limit
The Annals of Statistics, 2023 Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-$n$ asymptotics, i.e. as the amount of data increases.
Barthelme, Simon +3 more
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Gaussian Process Regression on Nested Spaces
SIAM/ASA Journal on Uncertainty Quantification, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christophette Blanchet-Scalliet +3 more
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Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller
IEEE Access, 2023 This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the
Hyuntae Kim, Hamin Chang
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Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression
Applied Sciences, 2022 During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-
Mahmood Ahmad +6 more
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Variational Tobit Gaussian Process Regression
Statistics and Computing, 2023 AbstractWe propose a variational inference-based framework for training a Gaussian process regression model subject to censored observational data. Data censoring is a typical problem encountered during the data gathering procedure and requires specialized techniques to perform inference since the resulting probabilistic models are typically ...
Marno Basson +2 more
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Estimation of clustering parameters using gaussian process regression. [PDF]
PLoS ONE, 2014 We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information ...
Paul Rigby +2 more
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